text: import torch
  import numpy as np
  from scipy.io.wavfile import read
  from scipy.io.wavfile import write
  
  import audio.stft as stft
  from audio.audio_processing import griffin_lim
  import hparams
  
  _stft = stft.TacotronSTFT(
      hparams.filter_length, hparams.hop_length, hparams.win_length,
      hparams.n_mel_channels, hparams.sampling_rate, hparams.mel_fmin,
      hparams.mel_fmax)
  
  
  def load_wav_to_torch(full_path):
      sampling_rate, data = read(full_path)
      return torch.FloatTensor(data.astype(np.float32)), sampling_rate
  
  
  def get_mel(filename):
      audio, sampling_rate = load_wav_to_torch(filename)
      if sampling_rate != _stft.sampling_rate:
          raise ValueError("{} {} SR doesn't match target {} SR".format(
              sampling_rate, _stft.sampling_rate))
      audio_norm = audio / hparams.max_wav_value
      audio_norm = audio_norm.unsqueeze(0)
      audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
      melspec, energy = _stft.mel_spectrogram(audio_norm)
      melspec = torch.squeeze(melspec, 0)
      energy = torch.squeeze(energy, 0)
      # melspec = torch.from_numpy(_normalize(melspec.numpy()))
  
      return melspec, energy
  
  
  def get_mel_from_wav(audio):
      sampling_rate = hparams.sampling_rate
      if sampling_rate != _stft.sampling_rate:
          raise ValueError("{} {} SR doesn't match target {} SR".format(
              sampling_rate, _stft.sampling_rate))
      audio_norm = audio / hparams.max_wav_value
      audio_norm = audio_norm.unsqueeze(0)
      audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
      melspec, energy = _stft.mel_spectrogram(audio_norm)
      melspec = torch.squeeze(melspec, 0)
      energy = torch.squeeze(energy, 0)
  
      return melspec, energy
  
  
  def inv_mel_spec(mel, out_filename, griffin_iters=60):
      mel = torch.stack([mel])
      # mel = torch.stack([torch.from_numpy(_denormalize(mel.numpy()))])
      mel_decompress = _stft.spectral_de_normalize(mel)
      mel_decompress = mel_decompress.transpose(1, 2).data.cpu()
      spec_from_mel_scaling = 1000
      spec_from_mel = torch.mm(mel_decompress[0], _stft.mel_basis)
      spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)
      spec_from_mel = spec_from_mel * spec_from_mel_scaling
  
      audio = griffin_lim(torch.autograd.Variable(
          spec_from_mel[:, :, :-1]), _stft.stft_fn, griffin_iters)
  
      audio = audio.squeeze()
      audio = audio.cpu().numpy()
      audio_path = out_filename
      write(audio_path, hparams.sampling_rate, audio)
  